@inproceedings{zhou-jurgens-2020-condolence,
    title = "Condolence and Empathy in Online Communities",
    author = "Zhou, Naitian  and
      Jurgens, David",
    editor = "Webber, Bonnie  and
      Cohn, Trevor  and
      He, Yulan  and
      Liu, Yang",
    booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = nov,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2020.emnlp-main.45/",
    doi = "10.18653/v1/2020.emnlp-main.45",
    pages = "609--626",
    abstract = "Offering condolence is a natural reaction to hearing someone`s distress. Individuals frequently express distress in social media, where some communities can provide support. However, not all condolence is equal{---}trite responses offer little actual support despite their good intentions. Here, we develop computational tools to create a massive dataset of 11.4M expressions of distress and 2.8M corresponding offerings of condolence in order to examine the dynamics of condolence online. Our study reveals widespread disparity in what types of distress receive supportive condolence rather than just engagement. Building on studies from social psychology, we analyze the language of condolence and develop a new dataset for quantifying the empathy in a condolence using appraisal theory. Finally, we demonstrate that the features of condolence individuals find most helpful online differ substantially in their features from those seen in interpersonal settings."
}